Adaptive Robust Traffic Engineering in Software Defined Networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.Comment: 10 pages, 8 figures, submitted to IFIP Networking 201

Big Data for Traffic Engineering in Software-Defined Networks

Software-defined networking overcomes the limitations of traditional networks by splitting the control plane from the data plane. The logic of the network is moved to a component called the controller that manages devices in the data plane. To implement this architecture, it has become the norm to use the OpenFlow (OF) protocol, which defines several counters maintained by network devices. These counters are the starting point for Traffic Engineering (TE) activities. TE monitors several network parameters, including network bandwidth utilization. A great challenge for TE is to collect and generate statistics about bandwidth utilization for monitoring and traffic analysis activities. This becomes even more challenging if fine-grained monitoring is required. Network management tasks such as network provisioning, capacity planning, load balancing, and anomaly detection can benefit from this fine-grained monitoring. Because the counters are updated for every packet that crosses the switch, they must be retrieved in a streaming fashion. This scenario suggests the use of Big Data streaming techniques to collect and process counter values. Therefore, this paper proposes an approach based on a fine-grained Big Data monitoring method to collect and generate traffic statistics using counter values. This research work can significantly leverage TE. The approach can provide a more detailed view of network resource utilization because it can deliver individual and aggregated statistical analyses of bandwidth consumption. Experimental results show the effectiveness of the proposed method

User-Centric Traffic Engineering in Software Defined Networks

Software defined networking (SDN) is a relatively new paradigm that decouples individual network elements from the control logic, offering real-time network programmability, translating high level policy abstractions into low level device configurations. The framework comprises of the data (forwarding) plane incorporating network devices, while the control logic and network services reside in the control and application planes respectively. Operators can optimize the network fabric to yield performance gains for individual applications and services utilizing flow metering and application-awareness, the default traffic management method in SDN. Existing approaches to traffic optimization, however, do not explicitly consider user application trends. Recent SDN traffic engineering designs either offer improvements for typical time-critical applications or focus on devising monitoring solutions aimed at measuring performance metrics of the respective services. The performance caveats of isolated service differentiation on the end users may be substantial considering the growth in Internet and network applications on offer and the resulting diversity in user activities. Application-level flow metering schemes therefore, fall short of fully exploiting the real-time network provisioning capability offered by SDN instead relying on rather static traffic control primitives frequent in legacy networking. For individual users, SDN may lead to substantial improvements if the framework allows operators to allocate resources while accounting for a user-centric mix of applications. This thesis explores the user traffic application trends in different network environments and proposes a novel user traffic profiling framework to aid the SDN control plane (controller) in accurately configuring network elements for a broad spectrum of users without impeding specific application requirements. This thesis starts with a critical review of existing traffic engineering solutions in SDN and highlights recent and ongoing work in network optimization studies. Predominant existing segregated application policy based controls in SDN do not consider the cost of isolated application gains on parallel SDN services and resulting consequence for users having varying application usage. Therefore, attention is given to investigating techniques which may capture the user behaviour for possible integration in SDN traffic controls. To this end, profiling of user application traffic trends is identified as a technique which may offer insight into the inherent diversity in user activities and offer possible incorporation in SDN based traffic engineering. A series of subsequent user traffic profiling studies are carried out in this regard employing network flow statistics collected from residential and enterprise network environments. Utilizing machine learning techniques including the prominent unsupervised k-means cluster analysis, user generated traffic flows are cluster analysed and the derived profiles in each networking environment are benchmarked for stability before integration in SDN control solutions. In parallel, a novel flow-based traffic classifier is designed to yield high accuracy in identifying user application flows and the traffic profiling mechanism is automated. The core functions of the novel user-centric traffic engineering solution are validated by the implementation of traffic profiling based SDN network control applications in residential, data center and campus based SDN environments. A series of simulations highlighting varying traffic conditions and profile based policy controls are designed and evaluated in each network setting using the traffic profiles derived from realistic environments to demonstrate the effectiveness of the traffic management solution. The overall network performance metrics per profile show substantive gains, proportional to operator defined user profile prioritization policies despite high traffic load conditions. The proposed user-centric SDN traffic engineering framework therefore, dynamically provisions data plane resources among different user traffic classes (profiles), capturing user behaviour to define and implement network policy controls, going beyond isolated application management

A QoS-based flow assignment for traffic engineering in software-defined networks

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs

A QoS-based flow assignment for traffic engineering in software-defined networks

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs

Network virtualization and traffic engineering in Software-Defined Networks

[ANGLÈS]Lately, the emerging paradigm of Software-Defined Networking has grown in presence and claims to simplify future networking. The decoupling of network control and forwarding plane proposed by this architecture allows the control of the entire network behavior by means of a logically centralized software program (controller). Such separation of planes opens the way to Network Virtualization, which provides users a logical abstraction of underlying network resources. However, network virtualization requires a mapping phase of the virtual resources over the physical resources, which is not trivial, formally known as the virtual network embedding problem. The present document focuses in the development of a variant of one of the proposed strategies to solve this critical step, prioritizing the real-time response. The proposed algorithm uses the properties offered by the Paths Algebra mathematical framework to provide a flexible environment where it is possible to combine any number of both linear and non-linear metrics. In addition, it is also used the multi-lexical ordination, a criterion to better distinguish paths that may be considered equal by other approaches. Such algorithm has been implemented as a software application that provides a simulation environment where the virtual network embedding process can be tested for any given topology. Subsequently, all the algorithm features have been checked in a set of performance tests, focusing on those oriented to the commitment among the real-time response and the quality of the embedding solutions. In general, testing results are very promising even in densely populated backbone topologies, where the number of alternative paths among each possible pair of origin and destination nodes grows exponentially.[CASTELLÀ] Software-Defined Networking (o Redes Definidas por Software) es un nuevo paradigma que tiene como objetivo simplificar la creación y gestión de redes de ordenadores. El desacoplamiento entre el control de la red y el plano de reenvío propuesto por esta arquitectura permite el control de todo el comportamiento de la red mediante un elemento lógico centralizado, llamado controlador. Esta separación de los planos abre la puerta a la virtualización de redes, proporcionando a los usuarios una abstracción lógica de los recursos de red subyacentes. Sin embargo, la virtualización de red requiere de una fase de asignación de los recursos virtuales a los recursos físicos, que no es trivial y que se conoce formalmente como el problema de incrustación de redes virtuales. El presente documento se centra en el desarrollo de una variante de una de las estrategias propuestas para resolver este paso crítico, dando prioridad a la respuesta en tiempo real. El algoritmo propuesto utiliza las propiedades ofrecidas por el marco matemático de Paths Algebra (o Álgebra de Caminos) para proporcionar un entorno flexible donde es posible combinar cualquier número de métricas lineales y no lineales. Además, también utiliza la ordenación multi-léxica, un criterio para distinguir mejor aquellos caminos que podrían ser considerados equivalentes por otros enfoques. Este algoritmo se ha implementado como una aplicación de software que proporciona un entorno de simulación en el que se puede probar el proceso de incrustación de redes virtuales para cualquier topología. Posteriormente, se han comprobado todas las características del algoritmo mediante un conjunto de pruebas de rendimiento, priorizando aquellas orientadas al compromiso entre la respuesta en tiempo real y la calidad de las soluciones de incrustación. En general, los resultados de las pruebas son muy prometedores incluso en topologías de redes troncales densamente pobladas, donde el número de caminos alternativos entre cada posible nodo origen y destino crece exponencialmente

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017

A qos based flow assignment for traffic engineering in software defined networks

In order to meet a tremendous amount of data storage requirement in next-generation wireless networks, an increasing number of cloud data centers has been deployed around the world. The underlying core networks are expected to provide the ability to store data in a dynamic and scalable computing environment. The traditional Internet Protocol (IP) has shown to be restricted due to its static architecture, which accordingly motivates the development of Software-Defined Networks (SDNs). In the SDNs, Traffic Engineering (TE) is simpler and programmable with a controller without the requirement of reconfiguration for all network devices. However, the existing TE algorithm of the SDNs rejects a number of requested flows caused by their undetermined routing paths where only flow bandwidth is considered in path determination. This paper proposes a Quality-of-Service (QoS) based Flow Assignment algorithm which enables the computation of end-to-end path for traffic flows guaranteeing the QoS requirements including bandwidth, end-to-end delay and packet loss probability. Through the Open Source Hybrid IP/SDNs platform, the proposed algorithm is validated and shown to significantly reduce flow rejection rate of up to 50% compared to the conventional approach, and therefore can be used to implement an effective DiffServ mechanism for flow allocation in the SDNs. Document type: Part of book or chapter of boo

Towards the Practical Implementation of Throughput-Optimal Traffic Engineering in Software Defined Networks

Broadband Wireless Networking topics: 5G, wireless underground sensor networks, software defined networkingThe new emerging networking paradigm of Software Defined Networks, a solution that separates the network control plane from the data forwarding plane, has been the main focus of recently research works. Nevertheless, Traffic Engineering is an important problem to optimize the network performance, especially for a centralized controlled network such as the SDN, by dynamically analyzing, predicting, and regulating the behavior of data transmitted over that network. Therefore, a first version of a new TE management tool called TECS-SENNA - Traffic Engineering Control System for SDN/OpenFlow Networks - is being developed. The connection of TEs with the tool provides a dynamically and globally optimized network resource allocation in such a way that the overall performance can be improved, including throughput, latency, stability, and load balancing, while satisfying the per-flow QoS requirements.El nuevo paradigma de redes emergente llamado Software Defined Networks, una solución que separa el plano de control del plano de envío de datos, ha sido el único foco principal de las recientes investigaciones. Sin embargo, la ingeniería de tráfico es un problema importante para optimizar el rendimiento de la red, especialmente para redes centralizadas y controladas como las redes SDN, analizando, prediciendo y regulando dinámicamente el comportamiento de los datos transmitidos a través de la red. Por eso, en este proyecto se ha construido una primera versión de una nueva herramienta de gestión de ingeniería de trafico llamada TECS-SENNA – Traffic Engineering Control System for SDN/OpenFlow Networkds. La conexión de ingeniería de tráfico con la herramienta proporciona una optimización de la asignación de recursos de red de manera global y dinámica, para que el rendimiento pueda ser mejorado, incluyendo la candencia, la latencia, la estabilidad y el equilibrio de carga, y satisfaciendo los requisitos por flujo de la cualidad de servicio.El nou paradigma de xarxes emergent anomenat Software Defined Networks, una solució que separa el pla de control del pla d’enviament de dades, ha estat l’únic focus principal de les recerques recentment. Però l’enginyeria de trànsit és un problema important per tal d’optimitzar el rendiment de la xarxa, especialment per xarxes centralitzades i controlades com les xarxes SDN, analitzant, fent prediccions i regulant dinàmicament el comportament de les dades transmeses a través de la xarxa. Per això, en aquest projecte s’ha construït una primera versió d’una nova eina de gestió d’enginyeria de trànsit anomenada TECS-SENNA – Traffic Engineering Control System for SDN/OpenFlow Networks. La connexió de l’enginyeria de trànsit amb l’eina proporciona una optimització de l’assignació de recursos de xarxa de manera global i dinàmica per tal que el rendiment pugui ser millorat, incloent la cadència, la latència, l’estabilitat i l’equilibri de càrrega i satisfent els requisits per flux de qualitat de servei

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017